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The 1920s saw the emergence of compartmental models. The Kermack–McKendrick epidemic model (1927) and the Reed–Frost epidemic model (1928) both describe the relationship between susceptible, infected and immune individuals in a population. The Kermack–McKendrick epidemic model was successful in predicting the behavior of outbreaks very similar to that observed in many recorded epidemics.[6]

The 1920s saw the emergence of compartmental models. The Kermack–McKendrick epidemic model (1927) and the Reed–Frost epidemic model (1928) both describe the relationship between susceptible, infected and immune individuals in a population. The Kermack–McKendrick epidemic model was successful in predicting the behavior of outbreaks very similar to that observed in many recorded epidemics.[6]

1920年代出现了仓室模型。  Kermack-McKendrick流行模型（1927年）和Reed-Frost流行模型（1928年）均描述了人群中易感，感染和免疫个体之间的关系。 Kermack–McKendrick流行模型成功地预测了暴发行为，与许多记录的流行病中观察到的行为非常相似。[6]

1920年代出现了仓室模型。  Kermack-McKendrick流行病模型（1927年）和Reed-Frost流行病模型（1928年）均描述了人群中易感，感染和免疫个体之间的关系。 Kermack–McKendrick流行病模型成功地预测了爆发行为，与许多已记录的流行病中观察到的行为非常相似。[6]

Recently, agent-based models (ABMs) have been used in exchange for simpler compartmental models, e.g.,[7]. For example, epidemiological ABMs have been used to inform public health (nonpharmaceutical) interventions against the spread of SARS-CoV-2[8]. Epidemiological ABMs, in spite of their complexity and requiring high computational power, have been criticized for simplifying and unrealistic assumptions[9][10]. Still, they can be useful in informing decisions regarding mitigation and suppression measures in cases when ABMs are accurately calibrated[11].

Recently, agent-based models (ABMs) have been used in exchange for simpler compartmental models, e.g.,[7]. For example, epidemiological ABMs have been used to inform public health (nonpharmaceutical) interventions against the spread of SARS-CoV-2[8]. Epidemiological ABMs, in spite of their complexity and requiring high computational power, have been criticized for simplifying and unrealistic assumptions[9][10]. Still, they can be useful in informing decisions regarding mitigation and suppression measures in cases when ABMs are accurately calibrated[11].

最近，基于代理的模型（ABM）已用于交换较简单的隔离模型，例如[7]。 例如，流行病学反弹道导弹已被用来告知公共卫生（非药物）干预措施，以防止SARS-CoV-2的传播[8]。 流行病学反弹道导弹尽管复杂且需要很高的计算能力，但由于其简化和不切实际的假设而受到批评[9] [10]。 不过，在准确校准ABM的情况下，它们对于通知有关缓解和抑制措施的决策还是有用的[11]。

最近，基于代理的模型（ABMs）已用于交换较简单的仓室模型，例如[7]。 例如，ABMs已被用于提供公共卫生方面的（非药物）干预措施，以防止SARS-CoV-2的传播[8]。 虽然ABMs复杂且需要很高的计算能力，但是该模型仍然由于其简化和不切实际的假设而受到批评[9] [10]。 不过，在准确校准ABMs的情况下，它们对于提供有关缓解和抑制措施方面的决策还是有用的[11]。

== 假设条件 Assumptions ==

== 假设条件 Assumptions ==

Models are only as good as the assumptions on which they are based. If a model makes predictions that are out of line with observed results and the mathematics is correct, the initial assumptions must change to make the model useful.

Models are only as good as the assumptions on which they are based. If a model makes predictions that are out of line with observed results and the mathematics is correct, the initial assumptions must change to make the model useful.

模型仅与它们所基于的假设一样好。 如果模型做出的预测与观察到的结果不符并且数学是正确的，则必须更改初始假设以使模型有用。

模型仅与它们所基于的假设一样好。 如果模型做出的预测与观察到的结果不符并且数学是正确的，则必须更改初始假设以使模型结果有其用处。

* Rectangular and stationary age distribution, i.e., everybody in the population lives to age L and then dies, and for each age (up to L) there is the same number of people in the population. This is often well-justified for developed countries where there is a low infant mortality and much of the population lives to the life expectancy.

* Rectangular and stationary age distribution, i.e., everybody in the population lives to age L and then dies, and for each age (up to L) there is the same number of people in the population. This is often well-justified for developed countries where there is a low infant mortality and much of the population lives to the life expectancy.

* 矩形和固定年龄分布，即人口中的每个人都活到L岁，然后死亡，并且对于每个年龄（至L），人口中的人数都是相同的。 对于婴儿死亡率较低且大部分人口都达到预期寿命的发达国家，这通常是合理的。

* 矩形和稳态时固定年龄分布，即人群中的每个人都活到L岁，然后死亡，并且对于每个年龄（至L），人群中的人数都是相同的。 对于婴儿死亡率较低且人群中大部分人都达到预期寿命的发达国家来说，这个假设通常是合理的。

* Homogeneous mixing of the population, i.e., individuals of the population under scrutiny assort and make contact at random and do not mix mostly in a smaller subgroup. This assumption is rarely justified because social structure is widespread. For example, most people in London only make contact with other Londoners. Further, within London then there are smaller subgroups, such as the Turkish community or teenagers (just to give two examples), who mix with each other more than people outside their group. However, homogeneous mixing is a standard assumption to make the mathematics tractable.

* Homogeneous mixing of the population, i.e., individuals of the population under scrutiny assort and make contact at random and do not mix mostly in a smaller subgroup. This assumption is rarely justified because social structure is widespread. For example, most people in London only make contact with other Londoners. Further, within London then there are smaller subgroups, such as the Turkish community or teenagers (just to give two examples), who mix with each other more than people outside their group. However, homogeneous mixing is a standard assumption to make the mathematics tractable.

* 群体的均匀混合，即，受到仔细检查的群体中的个体随机分配并进行接触，并且大多不在较小的亚组中混合。 由于社会结构广泛，这种假设很少成立。 例如，伦敦的大多数人只与其他伦敦人接触。 此外，在伦敦，则有较小的子群体，例如土耳其社区或青少年（仅举两个例子），他们的交往比其他人更多。 但是，均匀混合是使数学易于处理的标准假设。

* 群体的均匀混合，即受到仔细检查的群体中的个体随机分配并进行接触，并且大多不在较小的亚组中混合。 由于社会结构广泛，这种假设很少成立。 例如，伦敦的大多数人只与其他伦敦人接触。 此外，在伦敦，则有较小的子群体，例如土耳其社区或青少年（仅举两个例子），他们的交往比其他人更多。 但是，均匀混合是使数学易于处理的标准假设。